{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 鸢尾花数据分类示例\n",
    "## 步骤1：准备数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方式1：加载scikit-learn自带数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load_iris(返回的数据类型)： <class 'sklearn.utils.Bunch'>\n",
      "keys： dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n",
      "前3行数据：\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]]\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "前3行的分类标记： [0 0 0]\n",
      "['setosa' 'versicolor' 'virginica']\n",
      "<class 'sklearn.utils.Bunch'>\n",
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n",
      "data关键字对应的数据前3行：\n",
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "0                5.1               3.5                1.4               0.2\n",
      "1                4.9               3.0                1.4               0.2\n",
      "2                4.7               3.2                1.3               0.2\n",
      "frame关键字对应的数据前3行：\n",
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "0                5.1               3.5                1.4               0.2   \n",
      "1                4.9               3.0                1.4               0.2   \n",
      "2                4.7               3.2                1.3               0.2   \n",
      "\n",
      "   target  \n",
      "0       0  \n",
      "1       0  \n",
      "2       0  \n",
      "target关键字对应的数据前3行：\n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "Name: target, dtype: int32\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]]\n",
      "[0 0 0]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_data = load_iris()    #默认返回Bunch类型的对象\n",
    "print(\"load_iris(返回的数据类型)：\", type(iris_data))\n",
    "# 'sklearn.utils.Bunch'类似于字典，可以通过key来访问对应的值。\n",
    "print(\"keys：\",iris_data.keys())\n",
    "# 键data对应的对象是描述特征的数组，一共150个元素，这里取前3个元素。\n",
    "print(\"前3行数据：\\n\",iris_data[\"data\"][:3],sep=\"\")\n",
    "\n",
    "# 通过feature_names，可以查看各特征依次表示的含义。\n",
    "print(iris_data[\"feature_names\"])\n",
    "\n",
    "# 键target对应的对象是目标值（类别），一共150个，依次与data中的每个元素对应。\n",
    "print(\"前3行的分类标记：\", iris_data[\"target\"][:3])\n",
    "\n",
    "# 通过target_names，可以查看各目标值依次对应的含义。\n",
    "print(iris_data[\"target_names\"])\n",
    "\n",
    "# 如果导入的时候设置as_frame=True，\n",
    "# 那么data和frame两个键对应的都是特征数据构成的DataFrame对象，\n",
    "# data对应的数据不包含target列，frame对应的数据包含target列\n",
    "# 键target对应的是目标值构成的Series对象。\n",
    "iris_data = load_iris(as_frame=True)\n",
    "print(type(iris_data))\n",
    "print(iris_data.keys())\n",
    "#显示data、frame和target的前3行\n",
    "print(\"data关键字对应的数据前3行：\\n\",iris_data[\"data\"][:3],sep=\"\")\n",
    "print(\"frame关键字对应的数据前3行：\\n\",iris_data[\"frame\"][:3],sep=\"\")\n",
    "print(\"target关键字对应的数据前3行：\\n\",iris_data[\"target\"][:3],sep=\"\")\n",
    "\n",
    "# 如果导入数据时使用参数return_X_y=True，\n",
    "# 则返回特征数组和目标数组构成的元组，可以分别赋给两个变量。\n",
    "# 这种方式下无法查看各特征的名称和目标数值表示的分类名称。\n",
    "X, y = load_iris(return_X_y=True)\n",
    "print(X[:3])\n",
    "print(y[:3])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方式2：先下载，然后加载iris.data数据文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "读取的对象类型： <class 'pandas.core.frame.DataFrame'>\n",
      "DataFrame数据的前3行：\n",
      "   sepal length  sepal width  petal length  petal width        class\n",
      "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
      "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
      "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
      "随机打乱后，某一次运行的前3行：\n",
      "     sepal length  sepal width  petal length  petal width            class\n",
      "31            5.4          3.4           1.5          0.4      Iris-setosa\n",
      "145           6.7          3.0           5.2          2.3   Iris-virginica\n",
      "72            6.3          2.5           4.9          1.5  Iris-versicolor\n",
      "将分类文本标记替换为数字后的前3行：\n",
      "   sepal length  sepal width  petal length  petal width  class\n",
      "0           5.1          3.5           1.4          0.2      0\n",
      "1           4.9          3.0           1.4          0.2      0\n",
      "2           4.7          3.2           1.3          0.2      0\n",
      "前3个样本的特征数组：\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]]\n",
      "前3个样本的分类标记：\n",
      "[[0.]\n",
      " [0.]\n",
      " [0.]]\n"
     ]
    }
   ],
   "source": [
    "# 导入iris.data中鸢尾花数据集，并对数据进行转换\n",
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "# 通过pd.set_option设置DataFrame打印时右对齐\n",
    "pd.set_option(\"display.unicode.east_asian_width\",True)\n",
    "\n",
    "#pd.set_option(\"display.width\",500)  #设置较大的宽度，不换行\n",
    "#pd.set_option(\"display.max_columns\",4)  #最大显示列数\n",
    "#pd.set_option(\"display.max_columns\",None)  #显示所有列\n",
    "#pd.set_option(\"max_colwidth\",20)  #每列内容的显示宽度\n",
    "\n",
    "iris_data = pd.read_csv(\"data/iris.data\",header = None)\n",
    "print(\"读取的对象类型：\",type(iris_data))\n",
    "iris_data.columns = ['sepal length', 'sepal width', \n",
    "                     'petal length', 'petal width', 'class']\n",
    "print(\"DataFrame数据的前3行：\\n\", iris_data[:3],sep=\"\")\n",
    "\n",
    "# 打乱行顺序，存入新DataFrame对象中\n",
    "df = shuffle(iris_data)  \n",
    "print(\"随机打乱后，某一次运行的前3行：\\n\", df[:3],sep=\"\")\n",
    "# 也可以利用DataFrame.sample()采样的方法来打乱顺序，frac是要返回的比例\n",
    "#df1 = iris_data.sample(frac=1)  # frac=1表示返回全部，达到打乱顺序的效果\n",
    "#display(df1[:3])\n",
    "\n",
    "# 将class列中的字符串替换为数字\n",
    "df1 = iris_data.replace({\"class\":{'Iris-setosa': 0, \n",
    "                                  'Iris-versicolor': 1, \n",
    "                                  'Iris-virginica': 2}})\n",
    "print(\"将分类文本标记替换为数字后的前3行：\\n\", df1[:3],sep=\"\")\n",
    "# 打乱顺序\n",
    "#df2 = shuffle(df1)\n",
    "#display(df2[:3])\n",
    "\n",
    "# 获取特征数组\n",
    "X=df1.values[:,:4]\n",
    "print(\"前3个样本的特征数组：\\n\", X[:3],sep=\"\")\n",
    "# 获取分类标记数组\n",
    "y = df1.values[:,-1:]\n",
    "print(\"前3个样本的分类标记：\\n\", y[:3],sep=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方式3：在线加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "前两行：\n",
      "   sepal length  sepal width  petal length  petal width        class\n",
      "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
      "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
      "类别名称：['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n",
      "类别标签（编码）：\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "前两行：\n",
      "    sepal length  sepal width  petal length  petal width  class\n",
      "0           5.1          3.5           1.4          0.2      0\n",
      "1           4.9          3.0           1.4          0.2      0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "# 在线读取数据\n",
    "iris_data = pd.read_csv('https://archive.ics.uci.edu/'+\n",
    "        'ml/machine-learning-databases/iris/iris.data', header=None)\n",
    "iris_data.columns = ['sepal length', 'sepal width', \n",
    "                     'petal length', 'petal width', 'class']\n",
    "\n",
    "# 通过pd.set_option设置DataFrame打印时右对齐\n",
    "pd.set_option(\"display.unicode.east_asian_width\",True)\n",
    "print(\"前两行：\\n\",iris_data.head(2),sep=\"\")\n",
    "# np.unique中设置return_inverse=True，\n",
    "#  返回旧列表iris_data.iloc[:,-1:]中各类别名称在新列表iris_classes中的位置。\n",
    "#  这样，class_code中获得的就是各样本类别的编码\n",
    "iris_classes, class_code = np.unique(iris_data.iloc[:,-1:].values, \n",
    "                                     return_inverse=True) \n",
    "print(\"类别名称：\",iris_classes,sep=\"\")\n",
    "print(\"类别标签（编码）：\\n\",class_code,sep=\"\")\n",
    "iris_data[\"class\"] = class_code\n",
    "print(\"前两行：\\n\",iris_data.head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 步骤2：划分训练集与测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集特征与标签数据形状： (120, 4) (120,)\n",
      "测试集特征与标签数据形状： (30, 4) (30,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#这里重新加载数据，可以使用前面已加载的数据\n",
    "iris_data = load_iris()    #默认返回Bunch类型的对象\n",
    "#display(iris_data[\"data\"][:3])\n",
    "#display(iris_data[\"feature_names\"])\n",
    "#display(iris_data[\"target\"][:3])\n",
    "#display(iris_data[\"target_names\"])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                    iris_data.data, iris_data.target, \n",
    "                    test_size=0.2, random_state = 0)\n",
    "\n",
    "print(\"训练集特征与标签数据形状：\", X_train.shape, y_train.shape)\n",
    "print(\"测试集特征与标签数据形状：\", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 步骤3：训练数据可视化，直观了解训练集数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# 绘制训练数据的散点图矩阵\n",
    "X_train_df = pd.DataFrame(X_train, columns=iris_data.feature_names)\n",
    "\n",
    "pd.plotting.scatter_matrix(X_train_df, c=y_train,\n",
    "                figsize=(10,8), hist_kwds={\"bins\" : 20},\n",
    "                alpha = .9, s=80)      # s表示标记点大小\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 步骤4：构建模型（这里使用线性支持向量机）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(max_iter=10000, random_state=1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# 构建模型对象\n",
    "class_model = LinearSVC(random_state=1,\n",
    "                        max_iter=10000)  # max_iter表示最大迭代次数\n",
    "# 从训练数据集中学习模型参数\n",
    "class_model.fit(X_train, y_train)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 步骤5：评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集的预测分类： [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0]\n",
      "测试集的真实分类： [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0]\n",
      "训练集准确率： 0.9583333333333334\n",
      "测试集准确率： 1.0\n",
      "训练集准确率： 0.9583333333333334\n",
      "测试集准确率： 1.0\n",
      "混淆矩阵：\n",
      " [[11  0  0]\n",
      " [ 0 13  0]\n",
      " [ 0  0  6]]\n",
      "分类性能报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        11\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_train_predict = class_model.predict(X_train) \n",
    "y_test_predict = class_model.predict(X_test)\n",
    "print(\"测试集的预测分类：\", y_test_predict)\n",
    "print(\"测试集的真实分类：\", y_test)\n",
    "\n",
    "# 准确率计算方式1\n",
    "import numpy as np\n",
    "print(\"训练集准确率：\", np.mean(y_train_predict==y_train))\n",
    "print(\"测试集准确率：\", np.mean(y_test_predict==y_test))\n",
    "\n",
    "# 准确率计算方式2\n",
    "print(\"训练集准确率：\", class_model.score(X_train,y_train))\n",
    "print(\"测试集准确率：\", class_model.score(X_test,y_test))\n",
    "\n",
    "from sklearn import metrics\n",
    "# 打印混淆矩阵\n",
    "print(\"混淆矩阵：\\n\", \n",
    "      metrics.confusion_matrix(y_test,y_test_predict))\n",
    "# 打印分类性能报告\n",
    "print(\"分类性能报告：\\n\", \n",
    "      metrics.classification_report(y_test,y_test_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 步骤6：做出预测\n",
    "\n",
    "假设我们发现了一朵鸢尾花，花萼长4.5cm、宽2.8cm，花瓣长2.5cm、宽0.3cm。利用刚才学习的模型，判断新的鸢尾花属于哪个品种。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类型代码： [1]\n",
      "类型名称： ['versicolor']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# 为测量的四个特征数据构建一个二维的numpy数组，因为scikit-learn模型fit()方法输入参数为numpy二维数组类型\n",
    "X_new = np.array([[4.5, 2.8, 2.5, 0.3]])\n",
    "\n",
    "# 调用模型的pridect()方法来预测这个新的鸢尾花类型\n",
    "class_code = class_model.predict(X_new)\n",
    "print(\"类型代码：\", class_code)\n",
    "print(\"类型名称：\", iris_data[\"target_names\"][class_code])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存已训练的模型，可供下次直接使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./savedmodel/class_model.pkl']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将模型保存到文件\n",
    "import joblib\n",
    "joblib.dump(class_model,\"./savedmodel/class_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载已保存的模型，直接用于新数据的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类型代码： [1]\n",
      "类型名称： ['versicolor']\n"
     ]
    }
   ],
   "source": [
    "load_model = joblib.load(\"./savedmodel/class_model.pkl\")\n",
    "X_new = np.array([[4.5, 2.8, 2.5, 0.3]])\n",
    "class_code = load_model.predict(X_new)\n",
    "print(\"类型代码：\", class_code)\n",
    "print(\"类型名称：\", iris_data[\"target_names\"][class_code])"
   ]
  }
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